AUTHOR=Graf Katja , Murrieta-Coxca José Martin , Vogt Tobias , Besser Sophie , Geilen Daria , Kaden Tim , Bothe Anne-Katrin , Morales-Prieto Diana Maria , Amiri Behnam , Schaller Stephan , Kaufmann Ligaya , Raasch Martin , Ammar Ramy M. , Maass Christian 

TITLE=Digital twin-enhanced three-organ microphysiological system for studying drug pharmacokinetics in pregnant women

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1528748

DOI=10.3389/fphar.2025.1528748

ISSN=1663-9812

ABSTRACT=BackgroundPregnant women represent a vulnerable group in pharmaceutical research due to limited knowledge about drug metabolism and safety of commonly used corticosteroids like prednisone due to ethical and practical constraints. Current preclinical models, including animal studies, fail to accurately replicate human pregnancy conditions, resulting in gaps in drug safety and pharmacokinetics predictions. To address this issue, we used a three-organ microphysiological system (MPS) combined with a digital twin framework, to predict pharmacokinetics and fetal drug exposure.MethodsThe here shown human MPS integrated gut, liver, and placenta models, interconnected via the corresponding vasculature. Using prednisone as a model compound, we simulate oral drug administration and track its metabolism and transplacental transfer. To translate the generated data from MPS to human physiology, computational modelling techniques were developed.ResultsOur results demonstrate that the system maintains cellular integrity and accurately mimics in vivo drug dynamics, with predictions closely matching clinical data from pregnant women. Digital twinning closely aligned with the generated experimental data. Long-term exposure simulations confirmed the value of this integrated system for predicting the non-toxic metabolization of prednisone.ConclusionThis approach may provide a potential non-animal alternative that could contribute to our understanding of drug behavior during pregnancy and may support early-stage drug safety assessment for vulnerable populations.